Can unpredicted outcomes be intended? The role of outcome-beliefs in children's judgments of intention

An adult-like concept of intention includes a deliberate action to achieve a goal and a belief that one's action (if successful) will cause the desired outcome. For example, good outcomes caused by accident or by chance are not believed to be caused intentionally. In two experiments, we asked whether children understand this connection between intentions and outcomes. Children played two games in which actions could produce unintended outcomes (i.e., causes were unplanned). Children sometimes received a desirable reward independent of intention. In Experiment 1, 4- and 5-year-olds mistakenly claimed they had intended the desirable outcome even when it was unexpected. Four-year-olds judged that they had not intended a deliberate action if it did not yield a rewarding outcome. Experiment 2 demonstrates that 6-year-olds seldom make these errors. The results suggest that 4- and 5-year-old children have not yet attained an adult-like concept of intention. Their inaccurate judgments regarding their intentions, given a rewarding yet unexpected outcome, can be explained by a positivity bias.     

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.     

Causal inferences with group based trajectory models

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the impact of such events on developmental trajectories. The method draws upon two distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity scores. The essence of the method is to use the posterior probabilities of trajectory group membership from a finite mixture modeling framework, to create balance on lagged outcomes and other covariates established prior to t for the purpose of inferring the impact of first-time treatment at t on the outcome of interest. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montreal. The research has been supported by the National Science Foundation (NSF) (SES-99113700) and the National Institute of Mental Health (RO1 MH65611-01A2). It also made heavy use of data collected with the support from Québec’s CQRS and FCAR funding agencies, Canada’s NHRDP and SSHRC funding agencies, and the Molson Foundation. We thank Stephen Fienberg, Susan Murphy, Paul Rosenbaum, the editor, Paul De Boeck, and two anonymous reviewers for their insightful suggestions.     

Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.     

Error probabilities for inference of causal directions

A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods.     

Trained interpretive bias and anxiety

The relationship between anxiety and interpretive bias has been studied extensively, but the causal direction of this relationship remains largely unexplored. Do negative interpretations cause anxiety or is anxiety the cause of negative interpretations? Or are the two mutually reinforcing? The present study addressed this issue by experimentally inducing either a negative or a positive interpretive bias using Mathews and Mackintosh' [(2002). Induced emotional interpretation bias and anxiety. Journal of Abnormal Psychology, 109, 604-615] training paradigm and then examining its impact on state anxiety and anxiety vulnerability. In addition, it was investigated as to whether the interpretive bias was trained implicitly. Results indicated that style of interpreting could be manipulated. That is, when confronted with ambiguous information after the training, participants (n=118) interpreted this information congruent with their (positive or negative) training condition. Data on the issue of implicitness showed that participants tended to be explicitly aware of the valence of their training stimuli. Effects of trained interpretive bias on anxiety were only marginal and absent on anxiety vulnerability. It appears that interpretive bias can be trained reliably, but its effects on mood and vulnerability require further explanation.     

Bayes and blickets: effects of knowledge on causal induction in children and adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.     

Adjusting for the correspondence bias: Effects of causal uncertainty, cognitive busyness, and causal strength of situational information

This research examined the conditions under which people who have more chronic doubt about their ability to make sense of social behavior (i.e., are causally uncertain; [Weary and Edwards, 1994] and [Weary and Edwards, 1996]) are more likely to adjust their dispositional inferences for a target’s behaviors. Using a cognitive busyness manipulation within the attitude attribution paradigm, we found in Study 1 that higher causal uncertainty predicted increased correction of dispositional inferences, but only when participants had sufficient attentional resources to devote to the task. In Study 2, we found that higher-causal uncertainty predicted greater inferential correction, but only when the additional information provided a more compelling alternative explanation for the observed behavior. Results of this research are discussed in terms of their relevance to the Causal Uncertainty (Weary & Edwards, 1994) and dispositional inference models.     

Inference and coherence in causal-based artifact categorization

In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts’ associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact’s causal essence, and whether physical appearance or functional outcome is the most central artifact property.     

Who caused it? Interpersonal causal inferences in young children

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.     

假言推理的否定词效应

介绍假言推理中的4种否定词效应,以及相关的理论模型。被试在进行假言推理时,对前提或结论所表示的事件加进了否定词, 产生了一系列的心理效应。研究发现,范畴前提含有否定词,被试更难做出正确的推论;如果结论中含有否定词,被试却更容易做出正确的推论。为此,心理模型理论,规则理论,概率模型理论以及我国的学者对该问题都作了研究和阐述。